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Yi Ren

University of Oxford

4 papers hereh-index 11602 citations22 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • stat.ML1
affiliations
  • University of Oxford
Homepage
same name
  • Yi Ren — 23 papers
  • Yi Ren — 13 papers, h 10
  • Yi Ren — 8 papers, h 10
  • Yi Ren — 3 papers
  • Yi Ren — 3 papers, h 4
  • Yi Ren — 3 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192022
most citedCompositional Languages Emerge in a Neural Iterated Learning Model

34 citations · 60 across the 4 of their papers we have counts for

collaborators

4 papers

stat.ML2022★ 5 cited

Better Supervisory Signals by Observing Learning Paths

Yi Ren, Shangmin Guo, Danica J. Sutherland

Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing…

cs.CL2020★ 4 cited

Inductive Bias and Language Expressivity in Emergent Communication

Shangmin Guo, Yi Ren, Agnieszka Słowik +1

Referential games and reconstruction games are the most common game types for studying emergent languages. We investigate how the type of the language game affects the emergent lan…

cs.CL2020★ 34 cited

Compositional Languages Emerge in a Neural Iterated Learning Model

Yi Ren, Shangmin Guo, Matthieu Labeau +2

The principle of compositionality, which enables natural language to represent complex concepts via a structured combination of simpler ones, allows us to convey an open-ended set…

cs.CL2019★ 17 cited

The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents

Shangmin Guo, Yi Ren, Serhii Havrylov +3

Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, researc…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.